This study proposes a low-altitude wind prediction model for correcting the flight path plans of low-altitude aircraft. To solve large errors in numerical weather prediction (NWP) data and the inapplicability of high-altitude meteorological data to low altitude conditions, the model fuses the low-altitude lattice prediction data and the observation data of a specified ground international exchange station through the unscented Kalman filter (UKF)-based NWP interpretation technology to acquire the predicted low-altitude wind data. Subsequently, the model corrects the arrival times at the route points by combining the performance parameters of the aircraft according to the principle of velocity vector composition. Simulation experiment shows that the RMSEs of wind speed and direction acquired with the UKF prediction method are reduced by 12.88% and 17.50%, respectively, compared with the values obtained with the traditional Kalman filter prediction method. The proposed prediction model thus improves the accuracy of flight path planning in terms of time and space.
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